118 citations · 186 across the 14 of their papers we have counts for
15 papers
Energy, Information, and the Limits of Neuropsychiatric Classification
Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi +15
Importance: The search for reliable neuroimaging biomarkers of mental disorders has remained elusive despite decades of research. Why clinically consequential disorders yield weak…
deepmriprep: Voxel-based Morphometry (VBM) Preprocessing via Deep Neural Networks
Lukas Fisch, Nils R. Winter, Janik Goltermann +25
Voxel-based Morphometry (VBM) has emerged as a powerful approach in neuroimaging research, utilized in over 7,000 studies since the year 2000. Using Magnetic Resonance Imaging (MRI…
GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
Lukas Fisch, Michael O. Heming, Andreas Schulte-Mecklenbeck +12
Flow cytometry is widely used to identify cell populations in patient-derived fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While ubiquitous in research and cl…
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
Roberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng +84
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if…
Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks
Lukas Fisch, Stefan Zumdick, Carlotta Barkhau +8
Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research…
From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling
Jan Ernsting, Nils R. Winter, Ramona Leenings +13
The brain-age gap is one of the most investigated risk markers for brain changes across disorders. While the field is progressing towards large-scale models, recently incorporating…
More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities
Nils R. Winter, Ramona Leenings, Jan Ernsting +28
Introduction: Identifying neurobiological differences between patients suffering from Major Depressive Disorder (MDD) and healthy individuals has been a mainstay of clinical neuros…
Towards a Network Control Theory of Electroconvulsive Therapy Response
Tim Hahn, Hamidreza Jamalabadi, Erfan Nozari +24
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of…
A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder
Tim Hahn, Hamidreza Jamalabadi, Daniel Emden +31
Background: The evolution of symptoms over time is at the heart of understanding and treating mental disorders. However, a principled, quantitative framework explaining symptom dyn…
Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder
Tim Hahn, Nils R. Winter, Jan Ernsting +27
Background: A therapeutic intervention in psychiatry can be viewed as an attempt to influence the brain's large-scale, dynamic network state transitions underlying cognition and be…
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
Tim Hahn, Jan Ernsting, Nils R. Winter +31
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a corn…
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
Lukas Fisch, Jan Ernsting, Nils R. Winter +33
Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing…
The PHOTON Wizard -- Towards Educational Machine Learning Code Generators
Ramona Leenings, Nils Ralf Winter, Kelvin Sarink +4
Despite the tremendous efforts to democratize machine learning, especially in applied-science, the application is still often hampered by the lack of coding skills. As we consider…
PHOTONAI -- A Python API for Rapid Machine Learning Model Development
Ramona Leenings, Nils Ralf Winter, Lucas Plagwitz +12
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access…
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression
Claas Flint, Micah Cearns, Nils Opel +15
We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of m…